Example structure of a “good” guide
## Warning: Removed 1 rows containing non-finite values (stat_ydensity).
## [1] "mixed model failed: NCR_1411"
Per guide: * Per timepoint: t-test for difference in signal between no activator and 100 fM conditions per timepoint * Perform FDR correction for number of measurements from start of experiment to timepoint * Return first timepoint for which corrected p-value < 0.05
Structures of the two guides that performed well (rate > 1 above background) but without hairpin structure (NCR_1346, NCR_1351):
Determination of how much of predicted hairpin structure needs to be maintained:
## NCR.id spacer
## 120 NCR_1313 GUUUACCUUGGUAAUCAUCU
## 126 NCR_1319 UCAUUAAAUGGUAGGACAGG
## 137 NCR_1330 GCAAUCAAUGGGCAAGCUUU
## 138 NCR_1331 CUUCUCUGUAGCUAGUUGUA
## 139 NCR_1332 GAGUAAAUCUUCAUAAUUAG
## 142 NCR_1335 AUGGUGUCCAGCAAUACGAA
## 143 NCR_1336 GCCGUCUUUGUUAGCACCAU
## 155 NCR_1348 AUUAGCUCUCAGGUUGUCUA
## 156 NCR_1349 UGGUACGUUAAAAGUUGAUG
## 158 NCR_1351 UGGCUACUUUGAUACAAGGU
## 21685 NCR_1410 UGAAUGUAAAACUGAGGAUCUGAAAACU
## 9671 NCR_1412 UAUAAGCAAUUGUUAUCCAGAAAGGUAC
## 10691 NCR_1417 GAUUGAGAAACCACCUGUCUCCAUUUAU
## structure
## 120 ...((((((((.........))))................))))........
## 126 ...((((((((.........))))................))))........
## 137 .......(((((....(((...((........))..))).))))).......
## 138 ..(((.(((........(((((.........)))))......))).)))...
## 139 ...............(((((((..(((......)))...)))))))......
## 142 ................((..((((((((.....))))))))..)).......
## 143 ..............((((((((((........)))..)))))))........
## 155 (((((.((((......(((.((((............))))))))))))))))
## 156 ...((((((((.........))))........)))).((((((...))))))
## 158 (((.(((((((.........))))........))))))(((((...))))).
## 21685 ((((((.((((.........))))......((.....))........)).))))......
## 9671 ...(((.((((.........))))............(((....))).........)))..
## 10691 ...............(((((((((((......................)))))).)))))
## Warning in cor.test.default(GC_content, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in cor.test.default(GC_content, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in cor.test.default(downstream_U, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in cor.test.default(downstream_U, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in cor.test.default(downstream_unstructured_U, Estimate, method =
## "spearman"): Cannot compute exact p-value with ties
## Warning in cor.test.default(downstream_unstructured_U, Estimate, method =
## "spearman"): Cannot compute exact p-value with ties
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in cor.test.default(gRNA_MFE, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in cor.test.default(gRNA_MFE, Estimate, method = "spearman"): Cannot
## compute exact p-value with ties
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in predict.lm(training_fit, newdata = subset(lan_regression_data, :
## prediction from a rank-deficient fit may be misleading
## Likelihood ratio test
##
## Model 1: rate ~ spacer_1_A + spacer_1_C + spacer_1_G + spacer_1_U + spacer_2_A +
## spacer_2_C + spacer_2_G + spacer_2_U + spacer_3_A + spacer_3_C +
## spacer_3_G + spacer_3_U + spacer_4_A + spacer_4_C + spacer_4_G +
## spacer_4_U + spacer_5_A + spacer_5_C + spacer_5_G + spacer_5_U +
## spacer_6_A + spacer_6_C + spacer_6_G + spacer_6_U + spacer_7_A +
## spacer_7_C + spacer_7_G + spacer_7_U + spacer_8_A + spacer_8_C +
## spacer_8_G + spacer_8_U + spacer_9_A + spacer_9_C + spacer_9_G +
## spacer_9_U + spacer_10_A + spacer_10_C + spacer_10_G + spacer_10_U +
## spacer_11_A + spacer_11_C + spacer_11_G + spacer_11_U + spacer_12_A +
## spacer_12_C + spacer_12_G + spacer_12_U + spacer_13_A + spacer_13_C +
## spacer_13_G + spacer_13_U + spacer_14_A + spacer_14_C + spacer_14_G +
## spacer_14_U + spacer_15_A + spacer_15_C + spacer_15_G + spacer_15_U +
## spacer_16_A + spacer_16_C + spacer_16_G + spacer_16_U + spacer_17_A +
## spacer_17_C + spacer_17_G + spacer_17_U + spacer_18_A + spacer_18_C +
## spacer_18_G + spacer_18_U + spacer_19_A + spacer_19_C + spacer_19_G +
## spacer_19_U + spacer_20_A + spacer_20_C + spacer_20_G + spacer_20_U
## Model 2: rate ~ spacer_1_A + spacer_1_C + spacer_1_G + spacer_1_U + spacer_2_A +
## spacer_2_C + spacer_2_G + spacer_2_U + spacer_3_A + spacer_3_C +
## spacer_3_G + spacer_3_U + spacer_4_A + spacer_4_C + spacer_4_G +
## spacer_4_U + spacer_5_A + spacer_5_C + spacer_5_G + spacer_5_U +
## spacer_6_A + spacer_6_C + spacer_6_G + spacer_6_U + spacer_7_A +
## spacer_7_C + spacer_7_G + spacer_7_U + spacer_8_A + spacer_8_C +
## spacer_8_G + spacer_8_U + spacer_9_A + spacer_9_C + spacer_9_G +
## spacer_9_U + spacer_10_A + spacer_10_C + spacer_10_G + spacer_10_U +
## spacer_11_A + spacer_11_C + spacer_11_G + spacer_11_U + spacer_12_A +
## spacer_12_C + spacer_12_G + spacer_12_U + spacer_13_A + spacer_13_C +
## spacer_13_G + spacer_13_U + spacer_14_A + spacer_14_C + spacer_14_G +
## spacer_14_U + spacer_15_A + spacer_15_C + spacer_15_G + spacer_15_U +
## spacer_16_A + spacer_16_C + spacer_16_G + spacer_16_U + spacer_17_A +
## spacer_17_C + spacer_17_G + spacer_17_U + spacer_18_A + spacer_18_C +
## spacer_18_G + spacer_18_U + spacer_19_A + spacer_19_C + spacer_19_G +
## spacer_19_U + spacer_20_A + spacer_20_C + spacer_20_G + spacer_20_U +
## structure_1_. + structure_1_structured + structure_1_unstructured +
## structure_2_. + structure_2_both + structure_2_structured +
## structure_2_unstructured + structure_3_. + structure_3_both +
## structure_3_structured + structure_3_unstructured + structure_4_. +
## structure_4_both + structure_4_structured + structure_4_unstructured +
## structure_5_. + structure_5_both + structure_5_structured +
## structure_5_unstructured + structure_6_. + structure_6_both +
## structure_6_structured + structure_6_unstructured + structure_7_. +
## structure_7_both + structure_7_structured + structure_7_unstructured +
## structure_8_. + structure_8_both + structure_8_structured +
## structure_8_unstructured + structure_9_. + structure_9_both +
## structure_9_structured + structure_9_unstructured + structure_10_. +
## structure_10_both + structure_10_structured + structure_10_unstructured +
## structure_11_. + structure_11_both + structure_11_structured +
## structure_11_unstructured + structure_12_. + structure_12_both +
## structure_12_structured + structure_12_unstructured + structure_13_. +
## structure_13_both + structure_13_structured + structure_13_unstructured +
## structure_14_. + structure_14_both + structure_14_structured +
## structure_14_unstructured + structure_15_. + structure_15_both +
## structure_15_structured + structure_15_unstructured + structure_16_. +
## structure_16_both + structure_16_structured + structure_16_unstructured +
## structure_17_. + structure_17_both + structure_17_structured +
## structure_17_unstructured + structure_18_. + structure_18_both +
## structure_18_structured + structure_18_unstructured + structure_19_. +
## structure_19_both + structure_19_structured + structure_19_unstructured +
## structure_20_. + structure_20_both + structure_20_structured +
## structure_20_unstructured
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 62 -799.34
## 2 106 -763.56 44 71.567 0.005376 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
Model 1: sequence + structure
Model 2: only sequence
Model 3: only structure
Model 4: only sequence (binary)
Model 5: sequence (binary) + structure
Model 6: reduced features
##
## Call:
## glm(formula = Estimate ~ ., family = "gaussian", data = subset(model6_comparison_data_onehot,
## nchar(spacer) == 20, select = -spacer))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -33.749 -11.087 -3.329 8.129 61.562
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.6575 2.6309 8.992 2.76e-16 ***
## antitag_pos1_A 0.5839 3.1015 0.188 0.8509
## antitag_pos1_C 1.2439 3.5249 0.353 0.7246
## antitag_pos1_G -19.2342 3.4729 -5.538 1.03e-07 ***
## antitag_pos1_U NA NA NA NA
## downstream_unstructured_U 27.5652 16.0997 1.712 0.0885 .
## spacer_structure 8.3321 4.9108 1.697 0.0914 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 278.2812)
##
## Null deviance: 65043 on 191 degrees of freedom
## Residual deviance: 51760 on 186 degrees of freedom
## AIC: 1633.5
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = Estimate ~ ., family = "gaussian", data = subset(model6_comparison_data_onehot,
## select = -spacer))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -35.398 -11.142 -2.706 8.925 58.463
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.6891 2.5756 9.197 < 2e-16 ***
## antitag_pos1_A 0.4156 3.0177 0.138 0.8906
## antitag_pos1_C 1.7267 3.4167 0.505 0.6139
## antitag_pos1_G -17.9525 3.3852 -5.303 3.03e-07 ***
## antitag_pos1_U NA NA NA NA
## downstream_unstructured_U 28.4696 15.1970 1.873 0.0625 .
## spacer_structure 10.5638 4.7439 2.227 0.0271 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 279.8553)
##
## Null deviance: 68755 on 203 degrees of freedom
## Residual deviance: 55411 on 198 degrees of freedom
## AIC: 1736.2
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = (Estimate > 20) ~ ., family = "binomial", data = subset(model6_comparison_data_onehot,
## nchar(spacer) == 20, select = -spacer))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7623 -1.1627 0.5323 0.9851 2.2637
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.06303 0.33511 -0.188 0.8508
## antitag_pos1_A -0.07072 0.39452 -0.179 0.8577
## antitag_pos1_C 0.11281 0.44951 0.251 0.8018
## antitag_pos1_G -2.83803 0.61337 -4.627 3.71e-06 ***
## antitag_pos1_U NA NA NA NA
## downstream_unstructured_U 6.28612 2.44329 2.573 0.0101 *
## spacer_structure 1.05772 0.69890 1.513 0.1302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 265.65 on 191 degrees of freedom
## Residual deviance: 219.37 on 186 degrees of freedom
## AIC: 231.37
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = (Estimate > 20) ~ ., family = "binomial", data = subset(model6_comparison_data_onehot,
## select = -spacer))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8686 -1.1698 0.5773 0.9468 2.1114
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01795 0.33038 -0.054 0.95667
## antitag_pos1_A -0.16391 0.38765 -0.423 0.67241
## antitag_pos1_C 0.13186 0.44248 0.298 0.76570
## antitag_pos1_G -2.52240 0.53638 -4.703 2.57e-06 ***
## antitag_pos1_U NA NA NA NA
## downstream_unstructured_U 6.37941 2.34569 2.720 0.00654 **
## spacer_structure 1.26870 0.67542 1.878 0.06033 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 280.84 on 203 degrees of freedom
## Residual deviance: 235.10 on 198 degrees of freedom
## AIC: 247.1
##
## Number of Fisher Scoring iterations: 4
Top guides:
Bottom guides:
## Warning in eval(substitute(expr), data, enclos = parent.frame()): NAs introduced
## by coercion
## Warning: NAs introduced by coercion
## [1] "mixed model failed: NCR_1320"
## [1] "mixed model failed: NCR_1332"
## [1] "mixed model failed: NCR_1387"
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_smooth).
gBlock round 2 outlier: